Why Not Allow Individuals to Rank Freely? A Scaled Rank-Ordered Logit Approach Applied to Waste Management in Corsica

Since the introduction of the rank-ordered logit model in the eighties, the cognitive effort involved in a ranking task has been the source of concern. Despite the fact that the rank-ordered logit model provides efficiency gains when compared to the basic multinomial logit model, respondents may not all be able to provide a reliable full ranking of the alternatives they face. Unreliable or 'noisy' rankings result in estimate biases, so that it has even been suggested that only the first three ranks be used for estimation. In order to deal with this ranking capability issue, we propose a survey design which allows the respondents to provide freely incomplete rankings in accordance with their actual heterogeneous ranking capabilities. Using the full-ranking of the alternatives and the accurate sub-ranking of the alternatives, we first estimate a basic rank-ordered logit model. After testing for heteroscedasticity, we also estimate a heteroscedastic rank-ordered logit à la Hausman and Ruud (1987) and introduce a new scaled rank-ordered logit which allows us to model further the sources of heteroscedasticity in the data. The methodology is applied to the issue of waste management in Corsica. Using rankings of waste management options given by a representative sample of the Corsican population (530 respondents) we provide estimates of the willingness-to-pay for various options of waste management calculated from models estimated on the full-ranking and on the sub-ranking data. We find strong evidence that estimations on the full-ranking data set and on the accurate sub-ranking data set differ widely. Allowing individuals to provide freely incomplete rankings eliminates a large part of the heteroscedasticity stemming from heterogeneous ranking capabilities.